Medical imaging is generally recognized as key to better diagnosis and patient care. It has experienced explosive growth over the last few years due to imaging modalities such as X-ray, computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI). Conventionally, medical images have been inspected visually and the anatomic structures of interest or some lesions (tumours) are manually delineated by trained radiologists. However, the process can be tedious, time consuming and must be performed with great care to accurately delineate the object boundary. To replace some or all of the work of the radiologists, segmentation of medical image data is a prerequisite in computer-assisted diagnosis.
Medical image segmentation is a difficult task because in most cases it is very hard to separate the object from the image background. This is due to the nature of the image acquisition process in which noise is inherent for all medical data, as well as the grey-value mappings of the objects themselves. The resolution of every acquisition device is limited, thus the value of each voxel in medical image represents an averaged value over some neighbouring region, called the partial volume effect. Moreover, the characteristics of the object such as low contrast, small size or location of the object within an area of complicated anatomy bring more critical challenges for automatic segmentation. For example, the intensities of lesions (e.g. juxta-vascular nodule, juxta-pleural nodule or colon polyp) are very similar to the adjacent tissues (e.g. blood vessel or pleural wall). In this case, traditional intensity-based or model-based methods might not properly segment the object.